Unsupervised machine learning for the analytics of quantum devices
Unsupervised machine learning (ML), and in particular data clustering, is a powerful approach for the analysis of datasets and the identification of characteristic features occurring throughout a dataset. Due to its automated and largely unbiased character, it is gaining popularity across various scientific disciplines as the size of datasets has been steadily increasing over the last years.
Our aim is to develop novel unsupervised machine learning (ML) tools, both in terms of data segmentation, as well as the determination of the number of clusters. Our tools are employed for the classification of scientific datasets such as those acquired using the mechanically controllable break-junction (MCBJ) approach and Raman spectroscopy, both which are routinely used in the field of nanoscience to measure the electrical properties of individual molecules, and to investigate the vibrational properties of materials, respectively. An example of data clustering for a Raman Spectroscopy dataset using one of our ML approaches is shown in Figure 1. Here, a suspended graphene membrane is patterned using focused ion beam. Without beforehand knowledge of the system, the algorithm can identify the areas that received a different He+-exposure.